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{ |
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"cells": [ |
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{ |
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"cell_type": "code", |
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"execution_count": 3, |
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"id": "52dc600c-4c45-4803-81cb-f06347f4b2c3", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"import os\n", |
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"import requests\n", |
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"from dotenv import load_dotenv\n", |
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"from IPython.display import Markdown, display\n", |
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"from openai import OpenAI" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 4, |
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"id": "4082f16f-d843-41c7-9137-cdfec093b2d4", |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"name": "stdout", |
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"output_type": "stream", |
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"text": [ |
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"API key found and looks good so far\n" |
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] |
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} |
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], |
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"source": [ |
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"load_dotenv()\n", |
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"api_key = os.getenv('OPENAI_API_KEY')\n", |
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"\n", |
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"if not api_key:\n", |
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" print('No API key was found')\n", |
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"elif not api_key.startswith(\"sk-proj-\"):\n", |
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" print(\"API key is found but is not in the proper format\")\n", |
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"else:\n", |
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" print(\"API key found and looks good so far\")" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 5, |
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"id": "16c295ce-c57d-429e-8c03-f6610a8ddd42", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"openai = OpenAI()" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 16, |
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"id": "9a548a52-0f7e-4fdf-ad68-0138b2445935", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"system_prompt = \"\"\"You are a research summarizer. That summarizes the content of the research paper in no more than 1000 words. The research summary that you provide should include the following:\n", |
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"1) Title and Authors - Identify the study and contributors.\n", |
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"2) Objective/Problem - State the research goal or question.\n", |
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"3) Background - Briefly explain the context and significance.\n", |
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"4) Methods - Summarize the approach or methodology.\n", |
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"5) Key Findings - Highlight the main results or insights.\n", |
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"6) Conclusion - Provide the implications or contributions of the study.\n", |
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"7) Future Directions - Suggest areas for further research or exploration.\n", |
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"8) Limitations - Highlight constraints or challenges in the study.\n", |
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"9) Potential Applications - Discuss how the findings can be applied in real-world scenarios.\n", |
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"Keep all points concise, clear, and focused and generate output in markdown.\"\"\"" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 7, |
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"id": "66b4411f-172e-46be-b6cd-a9e5b857fb28", |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"name": "stdout", |
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"output_type": "stream", |
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"text": [ |
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"Requirement already satisfied: ipywidgets in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (8.1.5)\n", |
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"Requirement already satisfied: pdfplumber in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (0.11.4)\n", |
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"Requirement already satisfied: comm>=0.1.3 in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (from ipywidgets) (0.2.2)\n", |
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"Requirement already satisfied: ipython>=6.1.0 in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (from ipywidgets) (8.30.0)\n", |
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"Requirement already satisfied: traitlets>=4.3.1 in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (from ipywidgets) (5.14.3)\n", |
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"Requirement already satisfied: widgetsnbextension~=4.0.12 in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (from ipywidgets) (4.0.13)\n", |
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"Requirement already satisfied: jupyterlab_widgets~=3.0.12 in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (from ipywidgets) (3.0.13)\n", |
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"Requirement already satisfied: pdfminer.six==20231228 in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (from pdfplumber) (20231228)\n", |
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"Requirement already satisfied: Pillow>=9.1 in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (from pdfplumber) (11.0.0)\n", |
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"Requirement already satisfied: pypdfium2>=4.18.0 in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (from pdfplumber) (4.30.0)\n", |
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"Requirement already satisfied: charset-normalizer>=2.0.0 in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (from pdfminer.six==20231228->pdfplumber) (3.4.0)\n", |
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"Requirement already satisfied: cryptography>=36.0.0 in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (from pdfminer.six==20231228->pdfplumber) (44.0.0)\n", |
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"Requirement already satisfied: colorama in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (from ipython>=6.1.0->ipywidgets) (0.4.6)\n", |
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"Requirement already satisfied: decorator in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (from ipython>=6.1.0->ipywidgets) (5.1.1)\n", |
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"Requirement already satisfied: jedi>=0.16 in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (from ipython>=6.1.0->ipywidgets) (0.19.2)\n", |
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"Requirement already satisfied: matplotlib-inline in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (from ipython>=6.1.0->ipywidgets) (0.1.7)\n", |
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"Requirement already satisfied: prompt_toolkit<3.1.0,>=3.0.41 in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (from ipython>=6.1.0->ipywidgets) (3.0.48)\n", |
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"Requirement already satisfied: pygments>=2.4.0 in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (from ipython>=6.1.0->ipywidgets) (2.18.0)\n", |
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"Requirement already satisfied: stack_data in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (from ipython>=6.1.0->ipywidgets) (0.6.3)\n", |
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"Requirement already satisfied: typing_extensions>=4.6 in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (from ipython>=6.1.0->ipywidgets) (4.12.2)\n", |
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"Requirement already satisfied: cffi>=1.12 in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (from cryptography>=36.0.0->pdfminer.six==20231228->pdfplumber) (1.17.1)\n", |
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"Requirement already satisfied: parso<0.9.0,>=0.8.4 in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (from jedi>=0.16->ipython>=6.1.0->ipywidgets) (0.8.4)\n", |
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"Requirement already satisfied: wcwidth in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (from prompt_toolkit<3.1.0,>=3.0.41->ipython>=6.1.0->ipywidgets) (0.2.13)\n", |
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"Requirement already satisfied: executing>=1.2.0 in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (from stack_data->ipython>=6.1.0->ipywidgets) (2.1.0)\n", |
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"Requirement already satisfied: asttokens>=2.1.0 in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (from stack_data->ipython>=6.1.0->ipywidgets) (3.0.0)\n", |
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"Requirement already satisfied: pure_eval in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (from stack_data->ipython>=6.1.0->ipywidgets) (0.2.3)\n", |
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"Requirement already satisfied: pycparser in c:\\users\\legion\\anaconda3\\envs\\research_summary\\lib\\site-packages (from cffi>=1.12->cryptography>=36.0.0->pdfminer.six==20231228->pdfplumber) (2.22)\n", |
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"Note: you may need to restart the kernel to use updated packages.\n" |
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] |
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} |
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], |
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"source": [ |
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"pip install ipywidgets pdfplumber" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 8, |
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"id": "d8cd8556-ad86-4949-9f15-09de2b8c712b", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"import pdfplumber\n", |
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"from ipywidgets import widgets\n", |
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"from io import BytesIO" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 9, |
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"id": "0eba3cee-d85c-4d75-9b27-70c8cd7587b1", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"from IPython.display import display, Markdown" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 10, |
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"id": "53e270e1-c2e6-4bcc-9ada-90c059cd5a51", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"def messages_for(user_prompt):\n", |
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" return [\n", |
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" {\"role\": \"system\", \"content\": system_prompt},\n", |
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" {\"role\": \"user\", \"content\": user_prompt}\n", |
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" ]" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 11, |
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"id": "2f1807ec-c10b-4d26-9bee-89bd7a4bbb95", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"def summarize(user_prompt):\n", |
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" # Generate messages using the user_prompt\n", |
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" messages = messages_for(user_prompt)\n", |
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" try:\n", |
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" response = openai.chat.completions.create(\n", |
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" model=\"gpt-4o-mini\", # Correct model name\n", |
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" messages=messages,\n", |
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" max_tokens = 1000 # Pass the generated messages\n", |
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" )\n", |
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" # Return the content from the API response correctly\n", |
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" return response.choices[0].message.content\n", |
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" except Exception as e:\n", |
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" # Instead of printing, return an error message that can be displayed\n", |
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" return f\"Error in OpenAI API call: {e}\"" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 12, |
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"id": "0dee8345-4eec-4a9c-ac4e-ad70e13cea44", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"upload_widget = widgets.FileUpload(\n", |
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" accept='.pdf', \n", |
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" multiple=False,\n", |
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" description='Upload PDF',\n", |
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" layout=widgets.Layout(width='300px',height = '100px', border='2px dashed #cccccc', padding='10px')\n", |
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")" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 17, |
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"id": "1ff9c7b9-1a3a-4128-a33f-0e5bb2a93d33", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"def extract_text_and_generate_summary(change):\n", |
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" print(\"extracting text\")\n", |
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" if upload_widget.value:\n", |
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" # Extract the first uploaded file\n", |
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" uploaded_file = list(upload_widget.value)[0]\n", |
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" pdf_file = uploaded_file['content']\n", |
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"\n", |
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" # Extract text from the PDF\n", |
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" try:\n", |
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" with pdfplumber.open(BytesIO(pdf_file)) as pdf:\n", |
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" extracted_text = \"\\n\".join(page.extract_text() for page in pdf.pages)\n", |
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"\n", |
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" # Generate the user prompt\n", |
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" user_prompt = (\n", |
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" f\"You are looking at the text from a research paper. Summarize it in no more than 1000 words. \"\n", |
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" f\"The output should be in markdown.\\n\\n{extracted_text}\"\n", |
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" )\n", |
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"\n", |
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" # Get the summarized response\n", |
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" response = summarize(user_prompt)\n", |
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" \n", |
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" if response:\n", |
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" # Use IPython's display method to show markdown below the cell\n", |
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" display(Markdown(response))\n", |
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" \n", |
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" except Exception as e:\n", |
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" # If there's an error, display it using Markdown\n", |
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" display(Markdown(f\"**Error:** {str(e)}\"))\n", |
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"\n", |
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" # Reset the upload widget\n", |
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" upload_widget.value = ()" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 18, |
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"id": "0c16fe3f-704e-4a87-acd9-42c4e6b0d2fa", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"upload_widget.observe(extract_text_and_generate_summary, names='value')" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 19, |
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"id": "c2c2d2b2-1264-42d9-9271-c4700b4df80a", |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"data": { |
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"application/vnd.jupyter.widget-view+json": { |
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"model_id": "7304350377d845e78a9a758235e5eba1", |
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"version_major": 2, |
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"version_minor": 0 |
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}, |
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"text/plain": [ |
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"FileUpload(value=(), accept='.pdf', description='Upload PDF', layout=Layout(border_bottom='2px dashed #cccccc'…" |
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] |
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}, |
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"metadata": {}, |
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"output_type": "display_data" |
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} |
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], |
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"source": [ |
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"display(upload_widget)" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "70c76b90-e626-44b3-8d1f-6e995e8a938d", |
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"metadata": {}, |
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"source": [] |
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} |
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], |
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"metadata": { |
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"kernelspec": { |
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"display_name": "Python 3 (ipykernel)", |
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"language": "python", |
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"name": "python3" |
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}, |
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"language_info": { |
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"codemirror_mode": { |
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"name": "ipython", |
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"version": 3 |
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}, |
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"file_extension": ".py", |
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"mimetype": "text/x-python", |
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"name": "python", |
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"nbconvert_exporter": "python", |
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"pygments_lexer": "ipython3", |
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"version": "3.11.11" |
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} |
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}, |
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"nbformat": 4, |
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"nbformat_minor": 5 |
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} |
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{ |
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"id": "f61139a1-40e1-4273-b9a6-5a0a9d63a9bd", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"import requests\n", |
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"import json\n", |
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"from reportlab.lib.pagesizes import letter\n", |
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"from reportlab.pdfgen import canvas\n", |
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"from IPython.display import display, FileLink\n", |
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"from IPython.display import display, HTML, FileLink\n", |
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"from reportlab.lib.pagesizes import A4" |
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] |
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}, |
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{ |
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"id": "e0858b96-fd41-4911-a333-814e4ed23279", |
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"output_type": "stream", |
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"text": [ |
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"Collecting reportlab\n", |
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" Downloading reportlab-4.2.5-py3-none-any.whl.metadata (1.5 kB)\n", |
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"Requirement already satisfied: pillow>=9.0.0 in c:\\users\\legion\\anaconda3\\envs\\to_do_list\\lib\\site-packages (from reportlab) (11.0.0)\n", |
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"Collecting chardet (from reportlab)\n", |
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" Downloading chardet-5.2.0-py3-none-any.whl.metadata (3.4 kB)\n", |
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"Downloading reportlab-4.2.5-py3-none-any.whl (1.9 MB)\n", |
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"Downloading chardet-5.2.0-py3-none-any.whl (199 kB)\n", |
||||||
|
"Installing collected packages: chardet, reportlab\n", |
||||||
|
"Successfully installed chardet-5.2.0 reportlab-4.2.5\n" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"source": [ |
||||||
|
"!pip install reportlab" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 220, |
||||||
|
"id": "62cc9d37-c801-4e8a-ad2c-7b1450725a10", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"OLLAMA_API = \"http://localhost:11434/api/chat\"\n", |
||||||
|
"HEADERS = {\"Content-Type\":\"application/json\"}\n", |
||||||
|
"MODEL = \"llama3.2\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 249, |
||||||
|
"id": "525a81e7-30f8-4db7-bc8d-29948195bd4f", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"system_prompt = \"\"\"You are a to-do list generator. Based on the user's input, you will create a clear and descriptive to-do\n", |
||||||
|
"list using bullet points. Only generate the to-do list as bullet points with some explaination and time fraame only if asked for and nothing else. \n", |
||||||
|
"Be a little descriptive.\"\"\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 315, |
||||||
|
"id": "7fca3303-3add-468a-a6bd-be7a4d72c811", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"def generate_to_do_list(task_description):\n", |
||||||
|
" payload = {\n", |
||||||
|
" \"model\": MODEL,\n", |
||||||
|
" \"messages\": [\n", |
||||||
|
" {\"role\": \"system\", \"content\": system_prompt},\n", |
||||||
|
" {\"role\": \"user\", \"content\": task_description}\n", |
||||||
|
" ],\n", |
||||||
|
" \"stream\": False\n", |
||||||
|
" }\n", |
||||||
|
"\n", |
||||||
|
" response = requests.post(OLLAMA_API, json=payload, headers=HEADERS)\n", |
||||||
|
"\n", |
||||||
|
" if response.status_code == 200:\n", |
||||||
|
" try:\n", |
||||||
|
" json_response = response.json()\n", |
||||||
|
" to_do_list = json_response.get(\"message\", {}).get(\"content\", \"No to-do list found.\")\n", |
||||||
|
" \n", |
||||||
|
" formatted_output = \"Your To-Do List:\\n\\n\" + to_do_list\n", |
||||||
|
" file_name = \"to_do_list.txt\"\n", |
||||||
|
" \n", |
||||||
|
" with open(file_name, \"w\", encoding=\"utf-8\") as file:\n", |
||||||
|
" file.write(formatted_output)\n", |
||||||
|
"\n", |
||||||
|
" return file_name\n", |
||||||
|
" \n", |
||||||
|
" except Exception as e:\n", |
||||||
|
" return f\"Error parsing JSON: {e}\"\n", |
||||||
|
" else:\n", |
||||||
|
" return f\"Error: {response.status_code} - {response.text}\"" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 316, |
||||||
|
"id": "d45d6c7e-0e89-413e-8f30-e4975ea6d043", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [ |
||||||
|
{ |
||||||
|
"name": "stdin", |
||||||
|
"output_type": "stream", |
||||||
|
"text": [ |
||||||
|
"Enter the task description of the to-do list: Give me a 4-week to-do list plan for a wedding reception party.\n" |
||||||
|
] |
||||||
|
} |
||||||
|
], |
||||||
|
"source": [ |
||||||
|
"task_description = input(\"Enter the task description of the to-do list:\")" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 317, |
||||||
|
"id": "5493da44-e254-4d06-b973-a8069c2fc625", |
||||||
|
"metadata": { |
||||||
|
"scrolled": true |
||||||
|
}, |
||||||
|
"outputs": [], |
||||||
|
"source": [ |
||||||
|
"result = generate_to_do_list(task_description)" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": 318, |
||||||
|
"id": "5e95c722-ce1a-4630-b21a-1e00e7ba6ab9", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [ |
||||||
|
{ |
||||||
|
"data": { |
||||||
|
"text/html": [ |
||||||
|
"<p>You can download your to-do list by clicking the link below:</p>" |
||||||
|
], |
||||||
|
"text/plain": [ |
||||||
|
"<IPython.core.display.HTML object>" |
||||||
|
] |
||||||
|
}, |
||||||
|
"metadata": {}, |
||||||
|
"output_type": "display_data" |
||||||
|
}, |
||||||
|
{ |
||||||
|
"data": { |
||||||
|
"text/html": [ |
||||||
|
"<a href='to_do_list.txt' target='_blank'>to_do_list.txt</a><br>" |
||||||
|
], |
||||||
|
"text/plain": [ |
||||||
|
"C:\\Users\\Legion\\to-do list using ollama\\to_do_list.txt" |
||||||
|
] |
||||||
|
}, |
||||||
|
"metadata": {}, |
||||||
|
"output_type": "display_data" |
||||||
|
} |
||||||
|
], |
||||||
|
"source": [ |
||||||
|
"display(HTML(\"<p>You can download your to-do list by clicking the link below:</p>\"))\n", |
||||||
|
"display(FileLink(result))" |
||||||
|
] |
||||||
|
}, |
||||||
|
{ |
||||||
|
"cell_type": "code", |
||||||
|
"execution_count": null, |
||||||
|
"id": "f3d0a44e-bca4-4944-8593-1761c2f73a70", |
||||||
|
"metadata": {}, |
||||||
|
"outputs": [], |
||||||
|
"source": [] |
||||||
|
} |
||||||
|
], |
||||||
|
"metadata": { |
||||||
|
"kernelspec": { |
||||||
|
"display_name": "Python 3 (ipykernel)", |
||||||
|
"language": "python", |
||||||
|
"name": "python3" |
||||||
|
}, |
||||||
|
"language_info": { |
||||||
|
"codemirror_mode": { |
||||||
|
"name": "ipython", |
||||||
|
"version": 3 |
||||||
|
}, |
||||||
|
"file_extension": ".py", |
||||||
|
"mimetype": "text/x-python", |
||||||
|
"name": "python", |
||||||
|
"nbconvert_exporter": "python", |
||||||
|
"pygments_lexer": "ipython3", |
||||||
|
"version": "3.11.11" |
||||||
|
} |
||||||
|
}, |
||||||
|
"nbformat": 4, |
||||||
|
"nbformat_minor": 5 |
||||||
|
} |
Loading…
Reference in new issue